
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,cohen_kappa_score


df = pd.read_csv(r"C:\Users\ynaya\Downloads\parkinsons.csv")
df


import pandas_profiling as pr
pr.ProfileReport(df)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 195 entries, 0 to 194
Data columns (total 24 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   name              195 non-null    object 
 1   MDVP:Fo(Hz)       195 non-null    float64
 2   MDVP:Fhi(Hz)      195 non-null    float64
 3   MDVP:Flo(Hz)      195 non-null    float64
 4   MDVP:Jitter(%)    195 non-null    float64
 5   MDVP:Jitter(Abs)  195 non-null    float64
 6   MDVP:RAP          195 non-null    float64
 7   MDVP:PPQ          195 non-null    float64
 8   Jitter:DDP        195 non-null    float64
 9   MDVP:Shimmer      195 non-null    float64
 10  MDVP:Shimmer(dB)  195 non-null    float64
 11  Shimmer:APQ3      195 non-null    float64
 12  Shimmer:APQ5      195 non-null    float64
 13  MDVP:APQ          195 non-null    float64
 14  Shimmer:DDA       195 non-null    float64
 15  NHR               195 non-null    float64
 16  HNR               195 non-null    float64
 17  status            195 non-null    int64  
 18  RPDE              195 non-null    float64
 19  DFA               195 non-null    float64
 20  spread1           195 non-null    float64
 21  spread2           195 non-null    float64
 22  D2                195 non-null    float64
 23  PPE               195 non-null    float64
dtypes: float64(22), int64(1), object(1)
memory usage: 36.7+ KB


df.describe()


plt.figure(figsize=(10,10))
df["status"].value_counts().plot(kind="bar",color=['r','g'],alpha=0.6)

<AxesSubplot:>


plt.figure(figsize=(20,20))
for i,col in enumerate(df.columns[1:],1):
    plt.subplot(6,4,i)
    df[df.status==0][col].hist(bins=35, color='g', alpha=0.6,label="Healthy")
    df[df.status==1][col].hist(bins=35, color='r', alpha=0.6,label="Parkinson")
    plt.xlabel(col)
    plt.legend()


import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
rows=3
cols=7
fig, ax=plt.subplots(nrows=rows,ncols=cols,figsize=(16,4))
col=df.columns
index=1
for i in range(rows):
    for j in range(cols):
        sns.distplot(df[col[index]],ax=ax[i][j])
        index=index+1
        
plt.tight_layout()


cor = df.corr()
cor


plt.figure(figsize=(20,20))
sns.heatmap(cor,annot=True)

<AxesSubplot:>


df = df.drop(['name'],axis=1)
df


X = df.drop(['status'],axis=1)
X


Y = df['status']
Y

0      1
1      1
2      1
3      1
4      1
      ..
190    0
191    0
192    0
193    0
194    0
Name: status, Length: 195, dtype: int64


from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.15,random_state=4)


from sklearn.neighbors import KNeighborsClassifier

KNN = KNeighborsClassifier().fit(X_train,Y_train)

pred_train = KNN.predict(X_train)
pred_test = KNN.predict(X_test)

print("Train Accuracy Score : ", accuracy_score(Y_train, pred_train))
print("Test Accuracy Score :  ", accuracy_score(Y_test, pred_test))

print("\nTrain Confusion Matrix:\n ", confusion_matrix(Y_train, pred_train))
print("Test Confusion Matrix:\n ", confusion_matrix(Y_test, pred_test))

print('\nTrain Classification Report:')
print(classification_report (Y_train, pred_train))
print('\nTest Classification Report:')
print(classification_report (Y_test, pred_test))

Train Accuracy Score :  0.8909090909090909
Test Accuracy Score :   0.8

Train Confusion Matrix:
  [[ 24  14]
 [  4 123]]
Test Confusion Matrix:
  [[ 4  6]
 [ 0 20]]

Train Classification Report:
              precision    recall  f1-score   support

           0       0.86      0.63      0.73        38
           1       0.90      0.97      0.93       127

    accuracy                           0.89       165
   macro avg       0.88      0.80      0.83       165
weighted avg       0.89      0.89      0.88       165


Test Classification Report:
              precision    recall  f1-score   support

           0       1.00      0.40      0.57        10
           1       0.77      1.00      0.87        20

    accuracy                           0.80        30
   macro avg       0.88      0.70      0.72        30
weighted avg       0.85      0.80      0.77        30


print('Wrong predictions : ',(Y_test !=pred_test).sum(),'/',((Y_test == pred_test).sum()+(Y_test != pred_test).sum()))
print('Kappa Score:', cohen_kappa_score(Y_test,pred_test))

Wrong predictions :  6 / 30
Kappa Score: 0.47058823529411764


from sklearn.linear_model import LogisticRegression

lr = LogisticRegression().fit(X_train,Y_train)

pred_train = lr.predict(X_train)
pred_test = lr.predict(X_test)

print("Train Accuracy Score : ", accuracy_score(Y_train, pred_train))
print("Test Accuracy Score :  ", accuracy_score(Y_test, pred_test))

print("\nTrain Confusion Matrix:\n ", confusion_matrix(Y_train, pred_train))
print("Test Confusion Matrix:\n ", confusion_matrix(Y_test, pred_test))

print('\nTrain Classification Report:')
print(classification_report (Y_train, pred_train))
print('\nTest Classification Report:')
print(classification_report (Y_test, pred_test))

Train Accuracy Score :  0.8787878787878788
Test Accuracy Score :   0.7666666666666667

Train Confusion Matrix:
  [[ 23  15]
 [  5 122]]
Test Confusion Matrix:
  [[ 3  7]
 [ 0 20]]

Train Classification Report:
              precision    recall  f1-score   support

           0       0.82      0.61      0.70        38
           1       0.89      0.96      0.92       127

    accuracy                           0.88       165
   macro avg       0.86      0.78      0.81       165
weighted avg       0.87      0.88      0.87       165


Test Classification Report:
              precision    recall  f1-score   support

           0       1.00      0.30      0.46        10
           1       0.74      1.00      0.85        20

    accuracy                           0.77        30
   macro avg       0.87      0.65      0.66        30
weighted avg       0.83      0.77      0.72        30


print('Wrong predictions : ',(Y_test !=pred_test).sum(),'/',((Y_test == pred_test).sum()+(Y_test != pred_test).sum()))
print('Kappa Score:', cohen_kappa_score(Y_test,pred_test))

Wrong predictions :  7 / 30
Kappa Score: 0.36363636363636365


from sklearn.tree import DecisionTreeClassifier

dtc = DecisionTreeClassifier().fit(X_train,Y_train)

pred_train = dtc.predict(X_train)
pred_test = dtc.predict(X_test)

print("Train Accuracy Score : ", accuracy_score(Y_train, pred_train))
print("Test Accuracy Score :  ", accuracy_score(Y_test, pred_test))

print("\nTrain Confusion Matrix:\n ", confusion_matrix(Y_train, pred_train))
print("Test Confusion Matrix:\n ", confusion_matrix(Y_test, pred_test))

print('\nTrain Classification Report:')
print(classification_report (Y_train, pred_train))
print('\nTest Classification Report:')
print(classification_report (Y_test, pred_test))

Train Accuracy Score :  1.0
Test Accuracy Score :   0.9

Train Confusion Matrix:
  [[ 38   0]
 [  0 127]]
Test Confusion Matrix:
  [[ 8  2]
 [ 1 19]]

Train Classification Report:
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        38
           1       1.00      1.00      1.00       127

    accuracy                           1.00       165
   macro avg       1.00      1.00      1.00       165
weighted avg       1.00      1.00      1.00       165


Test Classification Report:
              precision    recall  f1-score   support

           0       0.89      0.80      0.84        10
           1       0.90      0.95      0.93        20

    accuracy                           0.90        30
   macro avg       0.90      0.88      0.88        30
weighted avg       0.90      0.90      0.90        30


print('Wrong predictions : ',(Y_test !=pred_test).sum(),'/',((Y_test == pred_test).sum()+(Y_test != pred_test).sum()))
print('Kappa Score:', cohen_kappa_score(Y_test,pred_test))

Wrong predictions :  3 / 30
Kappa Score: 0.7692307692307692


from sklearn.naive_bayes import GaussianNB

nb = GaussianNB().fit(X_train,Y_train)

pred_train = nb.predict(X_train)
pred_test = nb.predict(X_test)

print("Train Accuracy Score : ", accuracy_score(Y_train, pred_train))
print("Test Accuracy Score :  ", accuracy_score(Y_test, pred_test))

print("\nTrain Confusion Matrix:\n ", confusion_matrix(Y_train, pred_train))
print("Test Confusion Matrix:\n ", confusion_matrix(Y_test, pred_test))

print('\nTrain Classification Report:')
print(classification_report (Y_train, pred_train))
print('\nTest Classification Report:')
print(classification_report (Y_test, pred_test))

Train Accuracy Score :  0.7272727272727273
Test Accuracy Score :   0.7333333333333333

Train Confusion Matrix:
  [[36  2]
 [43 84]]
Test Confusion Matrix:
  [[ 8  2]
 [ 6 14]]

Train Classification Report:
              precision    recall  f1-score   support

           0       0.46      0.95      0.62        38
           1       0.98      0.66      0.79       127

    accuracy                           0.73       165
   macro avg       0.72      0.80      0.70       165
weighted avg       0.86      0.73      0.75       165


Test Classification Report:
              precision    recall  f1-score   support

           0       0.57      0.80      0.67        10
           1       0.88      0.70      0.78        20

    accuracy                           0.73        30
   macro avg       0.72      0.75      0.72        30
weighted avg       0.77      0.73      0.74        30


print('Wrong predictions : ',(Y_test !=pred_test).sum(),'/',((Y_test == pred_test).sum()+(Y_test != pred_test).sum()))
print('Kappa Score:', cohen_kappa_score(Y_test,pred_test))

Wrong predictions :  8 / 30
Kappa Score: 0.4545454545454546


from sklearn import svm

sv = svm.SVC(kernel="rbf").fit(X_train,Y_train)

pred_train = sv.predict(X_train)
pred_test = sv.predict(X_test)

print("Train Accuracy Score : ", accuracy_score(Y_train, pred_train))
print("Test Accuracy Score :  ", accuracy_score(Y_test, pred_test))

print("\nTrain Confusion Matrix:\n ", confusion_matrix(Y_train, pred_train))
print("Test Confusion Matrix:\n ", confusion_matrix(Y_test, pred_test))

print('\nTrain Classification Report:')
print(classification_report (Y_train, pred_train))
print('\nTest Classification Report:')
print(classification_report (Y_test, pred_test))

Train Accuracy Score :  0.8484848484848485
Test Accuracy Score :   0.6666666666666666

Train Confusion Matrix:
  [[ 13  25]
 [  0 127]]
Test Confusion Matrix:
  [[ 0 10]
 [ 0 20]]

Train Classification Report:
              precision    recall  f1-score   support

           0       1.00      0.34      0.51        38
           1       0.84      1.00      0.91       127

    accuracy                           0.85       165
   macro avg       0.92      0.67      0.71       165
weighted avg       0.87      0.85      0.82       165


Test Classification Report:
              precision    recall  f1-score   support

           0       0.00      0.00      0.00        10
           1       0.67      1.00      0.80        20

    accuracy                           0.67        30
   macro avg       0.33      0.50      0.40        30
weighted avg       0.44      0.67      0.53        30


print('Wrong predictions : ',(Y_test !=pred_test).sum(),'/',((Y_test == pred_test).sum()+(Y_test != pred_test).sum()))
print('Kappa Score:', cohen_kappa_score(Y_test,pred_test))

Wrong predictions :  10 / 30
Kappa Score: 0.0


from sklearn.ensemble import RandomForestClassifier

rfc = RandomForestClassifier().fit(X_train,Y_train)

pred_train = rfc.predict(X_train)
pred_test = rfc.predict(X_test)

print("Train Accuracy Score : ", accuracy_score(Y_train, pred_train))
print("Test Accuracy Score :  ", accuracy_score(Y_test, pred_test))

print("\nTrain Confusion Matrix:\n ", confusion_matrix(Y_train, pred_train))
print("Test Confusion Matrix:\n ", confusion_matrix(Y_test, pred_test))

print('\nTrain Classification Report:')
print(classification_report (Y_train, pred_train))
print('\nTest Classification Report:')
print(classification_report (Y_test, pred_test))

Train Accuracy Score :  1.0
Test Accuracy Score :   0.8

Train Confusion Matrix:
  [[ 38   0]
 [  0 127]]
Test Confusion Matrix:
  [[ 4  6]
 [ 0 20]]

Train Classification Report:
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        38
           1       1.00      1.00      1.00       127

    accuracy                           1.00       165
   macro avg       1.00      1.00      1.00       165
weighted avg       1.00      1.00      1.00       165


Test Classification Report:
              precision    recall  f1-score   support

           0       1.00      0.40      0.57        10
           1       0.77      1.00      0.87        20

    accuracy                           0.80        30
   macro avg       0.88      0.70      0.72        30
weighted avg       0.85      0.80      0.77        30


print('Wrong predictions : ',(Y_test !=pred_test).sum(),'/',((Y_test == pred_test).sum()+(Y_test != pred_test).sum()))
print('Kappa Score:', cohen_kappa_score(Y_test,pred_test))

Wrong predictions :  6 / 30
Kappa Score: 0.47058823529411764


import pickle

pickle.dump(dtc,open('parkin_model.pkl','wb'))


model = pickle.load(open('parkin_model.pkl','rb'))
mod = pd.DataFrame(Y_test)
mod['pred'] = model.predict(X_test)
mod.reset_index(drop=True)

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	phon_R01_S01_4	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	phon_R01_S01_5	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	phon_R01_S50_2	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	...	0.07008	0.02764	19.517	0	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	phon_R01_S50_3	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	...	0.04812	0.01810	19.147	0	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	phon_R01_S50_4	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	...	0.03804	0.10715	17.883	0	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	phon_R01_S50_5	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	...	0.03794	0.07223	19.020	0	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	phon_R01_S50_6	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	...	0.03078	0.04398	21.209	0	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
count	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	...	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000
mean	154.228641	197.104918	116.324631	0.006220	0.000044	0.003306	0.003446	0.009920	0.029709	0.282251	...	0.046993	0.024847	21.885974	0.753846	0.498536	0.718099	-5.684397	0.226510	2.381826	0.206552
std	41.390065	91.491548	43.521413	0.004848	0.000035	0.002968	0.002759	0.008903	0.018857	0.194877	...	0.030459	0.040418	4.425764	0.431878	0.103942	0.055336	1.090208	0.083406	0.382799	0.090119
min	88.333000	102.145000	65.476000	0.001680	0.000007	0.000680	0.000920	0.002040	0.009540	0.085000	...	0.013640	0.000650	8.441000	0.000000	0.256570	0.574282	-7.964984	0.006274	1.423287	0.044539
25%	117.572000	134.862500	84.291000	0.003460	0.000020	0.001660	0.001860	0.004985	0.016505	0.148500	...	0.024735	0.005925	19.198000	1.000000	0.421306	0.674758	-6.450096	0.174351	2.099125	0.137451
50%	148.790000	175.829000	104.315000	0.004940	0.000030	0.002500	0.002690	0.007490	0.022970	0.221000	...	0.038360	0.011660	22.085000	1.000000	0.495954	0.722254	-5.720868	0.218885	2.361532	0.194052
75%	182.769000	224.205500	140.018500	0.007365	0.000060	0.003835	0.003955	0.011505	0.037885	0.350000	...	0.060795	0.025640	25.075500	1.000000	0.587562	0.761881	-5.046192	0.279234	2.636456	0.252980
max	260.105000	592.030000	239.170000	0.033160	0.000260	0.021440	0.019580	0.064330	0.119080	1.302000	...	0.169420	0.314820	33.047000	1.000000	0.685151	0.825288	-2.434031	0.450493	3.671155	0.527367

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
MDVP:Fo(Hz)	1.000000	0.400985	0.596546	-0.118003	-0.382027	-0.076194	-0.112165	-0.076213	-0.098374	-0.073742	...	-0.094732	-0.021981	0.059144	-0.383535	-0.383894	-0.446013	-0.413738	-0.249450	0.177980	-0.372356
MDVP:Fhi(Hz)	0.400985	1.000000	0.084951	0.102086	-0.029198	0.097177	0.091126	0.097150	0.002281	0.043465	...	-0.003733	0.163766	-0.024893	-0.166136	-0.112404	-0.343097	-0.076658	-0.002954	0.176323	-0.069543
MDVP:Flo(Hz)	0.596546	0.084951	1.000000	-0.139919	-0.277815	-0.100519	-0.095828	-0.100488	-0.144543	-0.119089	...	-0.150737	-0.108670	0.210851	-0.380200	-0.400143	-0.050406	-0.394857	-0.243829	-0.100629	-0.340071
MDVP:Jitter(%)	-0.118003	0.102086	-0.139919	1.000000	0.935714	0.990276	0.974256	0.990276	0.769063	0.804289	...	0.746635	0.906959	-0.728165	0.278220	0.360673	0.098572	0.693577	0.385123	0.433434	0.721543
MDVP:Jitter(Abs)	-0.382027	-0.029198	-0.277815	0.935714	1.000000	0.922911	0.897778	0.922913	0.703322	0.716601	...	0.697170	0.834972	-0.656810	0.338653	0.441839	0.175036	0.735779	0.388543	0.310694	0.748162
MDVP:RAP	-0.076194	0.097177	-0.100519	0.990276	0.922911	1.000000	0.957317	1.000000	0.759581	0.790652	...	0.744919	0.919521	-0.721543	0.266668	0.342140	0.064083	0.648328	0.324407	0.426605	0.670999
MDVP:PPQ	-0.112165	0.091126	-0.095828	0.974256	0.897778	0.957317	1.000000	0.957319	0.797826	0.839239	...	0.763592	0.844604	-0.731510	0.288698	0.333274	0.196301	0.716489	0.407605	0.412524	0.769647
Jitter:DDP	-0.076213	0.097150	-0.100488	0.990276	0.922913	1.000000	0.957319	1.000000	0.759555	0.790621	...	0.744901	0.919548	-0.721494	0.266646	0.342079	0.064026	0.648328	0.324377	0.426556	0.671005
MDVP:Shimmer	-0.098374	0.002281	-0.144543	0.769063	0.703322	0.759581	0.797826	0.759555	1.000000	0.987258	...	0.987626	0.722194	-0.835271	0.367430	0.447424	0.159954	0.654734	0.452025	0.507088	0.693771
MDVP:Shimmer(dB)	-0.073742	0.043465	-0.119089	0.804289	0.716601	0.790652	0.839239	0.790621	0.987258	1.000000	...	0.963202	0.744477	-0.827805	0.350697	0.410684	0.165157	0.652547	0.454314	0.512233	0.695058
Shimmer:APQ3	-0.094717	-0.003743	-0.150747	0.746625	0.697153	0.744912	0.763580	0.744894	0.987625	0.963198	...	1.000000	0.716207	-0.827123	0.347617	0.435242	0.151124	0.610967	0.402243	0.467265	0.645377
Shimmer:APQ5	-0.070682	-0.009997	-0.101095	0.725561	0.648961	0.709927	0.786780	0.709907	0.982835	0.973751	...	0.960072	0.658080	-0.813753	0.351148	0.399903	0.213873	0.646809	0.457195	0.502174	0.702456
MDVP:APQ	-0.077774	0.004937	-0.107293	0.758255	0.648793	0.737455	0.804139	0.737439	0.950083	0.960977	...	0.896647	0.694019	-0.800407	0.364316	0.451379	0.157276	0.673158	0.502188	0.536869	0.721694
Shimmer:DDA	-0.094732	-0.003733	-0.150737	0.746635	0.697170	0.744919	0.763592	0.744901	0.987626	0.963202	...	1.000000	0.716215	-0.827130	0.347608	0.435237	0.151132	0.610971	0.402223	0.467261	0.645389
NHR	-0.021981	0.163766	-0.108670	0.906959	0.834972	0.919521	0.844604	0.919548	0.722194	0.744477	...	0.716215	1.000000	-0.714072	0.189429	0.370890	-0.131882	0.540865	0.318099	0.470949	0.552591
HNR	0.059144	-0.024893	0.210851	-0.728165	-0.656810	-0.721543	-0.731510	-0.721494	-0.835271	-0.827805	...	-0.827130	-0.714072	1.000000	-0.361515	-0.598736	-0.008665	-0.673210	-0.431564	-0.601401	-0.692876
status	-0.383535	-0.166136	-0.380200	0.278220	0.338653	0.266668	0.288698	0.266646	0.367430	0.350697	...	0.347608	0.189429	-0.361515	1.000000	0.308567	0.231739	0.564838	0.454842	0.340232	0.531039
RPDE	-0.383894	-0.112404	-0.400143	0.360673	0.441839	0.342140	0.333274	0.342079	0.447424	0.410684	...	0.435237	0.370890	-0.598736	0.308567	1.000000	-0.110950	0.591117	0.479905	0.236931	0.545886
DFA	-0.446013	-0.343097	-0.050406	0.098572	0.175036	0.064083	0.196301	0.064026	0.159954	0.165157	...	0.151132	-0.131882	-0.008665	0.231739	-0.110950	1.000000	0.195668	0.166548	-0.165381	0.270445
spread1	-0.413738	-0.076658	-0.394857	0.693577	0.735779	0.648328	0.716489	0.648328	0.654734	0.652547	...	0.610971	0.540865	-0.673210	0.564838	0.591117	0.195668	1.000000	0.652358	0.495123	0.962435
spread2	-0.249450	-0.002954	-0.243829	0.385123	0.388543	0.324407	0.407605	0.324377	0.452025	0.454314	...	0.402223	0.318099	-0.431564	0.454842	0.479905	0.166548	0.652358	1.000000	0.523532	0.644711
D2	0.177980	0.176323	-0.100629	0.433434	0.310694	0.426605	0.412524	0.426556	0.507088	0.512233	...	0.467261	0.470949	-0.601401	0.340232	0.236931	-0.165381	0.495123	0.523532	1.000000	0.480585
PPE	-0.372356	-0.069543	-0.340071	0.721543	0.748162	0.670999	0.769647	0.671005	0.693771	0.695058	...	0.645389	0.552591	-0.692876	0.531039	0.545886	0.270445	0.962435	0.644711	0.480585	1.000000

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	0.426	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	0.626	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	0.482	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	0.517	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	0.584	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	0.405	...	0.07008	0.02764	19.517	0	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	0.263	...	0.04812	0.01810	19.147	0	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	0.256	...	0.03804	0.10715	17.883	0	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	0.241	...	0.03794	0.07223	19.020	0	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	0.190	...	0.03078	0.04398	21.209	0	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	MDVP:APQ	Shimmer:DDA	NHR	HNR	RPDE	DFA	spread1	spread2	D2	PPE
0	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	0.426	...	0.02971	0.06545	0.02211	21.033	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	0.626	...	0.04368	0.09403	0.01929	19.085	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	0.482	...	0.03590	0.08270	0.01309	20.651	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	0.517	...	0.03772	0.08771	0.01353	20.644	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	0.584	...	0.04465	0.10470	0.01767	19.649	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	0.405	...	0.02745	0.07008	0.02764	19.517	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	0.263	...	0.01879	0.04812	0.01810	19.147	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	0.256	...	0.01667	0.03804	0.10715	17.883	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	0.241	...	0.01588	0.03794	0.07223	19.020	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	0.190	...	0.01373	0.03078	0.04398	21.209	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

Load Dataset¶

Profile Report¶

Manual EDA¶

Build Model¶

K Nearest Neighbors¶

Logistic Regression¶

Decision Tree¶

Naive Bayes¶

Support Vector Machine¶

Random Forest Classifier¶

Store and Load the Model¶

	status	pred
0	1	1
1	1	0
2	0	0
3	0	0
4	1	1
5	1	1
6	1	1
7	0	1
8	0	1
9	1	1
10	0	0
11	1	1
12	1	1
13	0	0
14	1	1
15	0	0
16	1	1
17	0	0
18	1	1
19	1	1
20	1	1
21	1	1
22	1	1
23	0	0
24	1	1
25	1	1
26	1	1
27	1	1
28	1	1
29	0	0

	status	pred
0	1	1
1	1	0
2	0	0
3	0	0
4	1	1
5	1	1
6	1	1
7	0	1
8	0	1
9	1	1
10	0	0
11	1	1
12	1	1
13	0	0
14	1	1
15	0	0
16	1	1
17	0	0
18	1	1
19	1	1
20	1	1
21	1	1
22	1	1
23	0	0
24	1	1
25	1	1
26	1	1
27	1	1
28	1	1
29	0	0

	status	pred
0	1	1
1	1	0
2	0	0
3	0	0
4	1	1
5	1	1
6	1	1
7	0	1
8	0	1
9	1	1
10	0	0
11	1	1
12	1	1
13	0	0
14	1	1
15	0	0
16	1	1
17	0	0
18	1	1
19	1	1
20	1	1
21	1	1
22	1	1
23	0	0
24	1	1
25	1	1
26	1	1
27	1	1
28	1	1
29	0	0